Advance Driver Assistance and Monitoring System (ADAMS)

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A Smart and Adaptive Driver Monitoring and Assistance System based on Facial Landmarking & Deep Learning.

The problem ADAMS-Advanced Driving And Monitoring System solves is that a large portion of road accidents (78.4%) are due to the driver fatigue and inattention. This includes speeding, driving under the influence of alcohol or drugs, and hit and run cases. Other causes of road accidents are the fault of cyclists, pedestrians, or drivers of other vehicles. Fewer accidents are caused due to neglect of civic bodies (2.8%), defects in motor vehicles (2.3%).

What it does:

  1. Facial Recognition
  2. Emotion Recognition
  3. Drowsiness Detection
  4. Head Movement
  5. Object Detection
  6. Blind Spot Detection

How we built it:
So, to avoid this we have come up with the idea of ADAS. Advanced Driver-Assistance Systems (ADAS) are electronic systems that assist drivers in driving and parking functions. Through a safe human-machine interface, ADAS increase car and road safety.

Our Advanced Driver Assistance Systems usage is not limited to cars; it can be used in any vehicle be it trucks, buses, etc.

Our ADAS has facial recognition, emotion recognition, drowsiness detection, head position detection, object detection, blind spot detection and even driving duration as metrics to avoid accidents.

GitHub Link :


Voting is closed!


  • Name:
    Anshuman Phadke
  • Type of entry:
    Team members:
    Anshuman Phadke
    Saksham Bhutani
    Arvind N
  • Software used for this entry:
    Python,Tensorflow,Keras,Open CV,Arduino IDE
  • Patent status: